Deep Saline Aquifers
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2021 ◽  
Vol 3 ◽  
Yashvardhan Verma ◽  
Vikram Vishal ◽  
P. G. Ranjith

In order to tackle the exponential rise in global CO2 emissions, the Intergovernmental Panel on Climate Change (IPCC) proposed a carbon budget of 2,900 Gt to limit the rise in global temperature levels to 2°C above the pre-industrial level. Apart from curbing our emissions, carbon sequestration can play a significant role in meeting these ambitious goals. More than 500 Gt of CO2 will need to be stored underground by the end of this century to make a meaningful impact. Global capacity for CO2 storage far exceeds this requirement, the majority of which resides in unexplored deep aquifers. To identify potential storage sites and quantify their storage capacities, prospective aquifers or reservoirs need to be screened based on properties that affect the retention of CO2 in porous rocks. Apart from the total volume of a reservoir, the storage potential is largely constrained by an increase in pore pressure during the early years of injection and by migration of the CO2 plume in the long term. The reservoir properties affect both the pressure buildup and the plume front below the caprock. However, not many studies have quantified these effects. The current analysis computes the effect of rock properties (porosity, permeability, permeability anisotropy, pore compressibility, and formation water salinity) and injection rate on both these parameters by simulating CO2 injection at the bottom of a 2D mesh grid with hydrostatic boundary conditions. The study found that the most significant property in the sensitivity analysis was permeability. Porosity too affected the CO2 plume migration substantially, with higher porosities considerably delaying horizontal and vertical migration. Injection rate impacted both the pressure rise and plume migration consistently. Thus, in screening potential storage sites, we can infer that permeability is the dominant criterion when the pore pressure is closer to the minimum principal stress in the rocks, due to which injection rate needs to be managed with greater caution. Porosity is more significant when the lateral extents of the reservoir limit the storage potential.

2021 ◽  
Hung Vo-Thanh ◽  
Kang-Kun Lee

Abstract Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

Naimish Agarwal

Abstract: More than ever, the fate of anthropogenic CO2 emissions is in our hands. Since the advent of industrialization, there has been an increase in the use of fossil fuels to fulfil rising energy demands. The usage of such fuels results in the release of carbon dioxide (CO2) and other greenhouse gases, which result in increased temperature. Such warming is extremely harmful to life on Earth. The development of technology to counter the climate change and spreading it for widespread adoptions. We need to establish a framework to provide overarching guidance for the well-functioning of technology and mechanism development of Carbon Capture and Storage. Carbon capture and storage (CCS) is widely regarded as a critical approach for achieving the desired CO2 emission reduction. Various elements of CCS, such as state-of-the-art technology for CO2 collection, separation, transport, storage, politics, opportunities, and innovations, are examined and explored in this paper. Carbon capture and storage is the process of capturing and storing carbon dioxide (CO2) before it is discharged into the environment (CCS). The technology can capture high amounts of CO2 produced by fossil fuel combustion in power plants and industrial processes. CO2 is compressed and transferred by pipeline, ship, or road tanker once it has been captured. CO2 can then be piped underground, usually to depths of 1km or more, and stored in depleted oil and gas reservoirs, coalbeds, or deep saline aquifers, depending on the geology. CO2 could also be used to produce commercially marketable products. With the goal of keeping world average temperatures below 1.5°C (2.7°F) and preventing global average temperature rises of more than 2°C (3.6°F) over pre-industrial levels, CCS model should be our priority to be implemented with the proper economical map

2021 ◽  
Vol 5 (3) ◽  
pp. 333-346
Jie Ren ◽  
Yuan Wang ◽  
Di Feng ◽  
Jiakun Gong

2021 ◽  
Hung Vo Thanh ◽  
Kang-Kun Lee

Abstract Deep saline formations are considered as potential sites for geological carbon storage (GCS). To better understand the CO2 trapping mechanism in saline aquifers, it is necessary to develop robust tools to evaluate CO2 trapping efficiency. This paper introduces the application of Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF) to predict CO2 trapping efficiency in saline formations. First, the uncertainty variables, including geologic parameters, petrophysical properties, and other physical characteristics data were utilized to create a training dataset. A total of 101 reservoir simulation samples were then performed, and the residual trapping, solubility trapping, and cumulative CO2 injection were collected. The predicted results indicate that three machine learning (ML) models that evaluate performance from high to low: GPR, SVM, and RF can be selected to predict the CO2 trapping efficiency in deep saline formations. The GPR model has an excellent CO2 trapping prediction efficiency with the highest correlation factor (R2 = 0.992) and lowest root mean square error (RMSE = 0.00491). The accuracy and stability of the GPR models were verified for an actual reservoir in offshore Vietnam. The predictive models obtained a good agreement between the simulated field and the predicted trapping index. These findings indicate that the GPR ML models can support the numerical simulation as a robust predictive tool for estimating the performance of CO2 trapping in the subsurface.

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